Method for determining retail unit specific price sensitivities

ABSTRACT

A method of determining a price sensitivity index for one or more retail units is based on the relation between profits, sales or traffic and a fixed weight price index based on information from individual retail units. Statistical regression and the theory of the single-product firm is used to analyze the relation between changes in performance variables and changes in the price index, leading to a unit-specific index of sensitivity. This information allows stores to be sorted into those which can see price aggression, those which cannot, and those which are likely to respond to promotions.

[0001] This application claims priority under 35 USC 119(e) based onprovisional patent application No. 60/331,213 filed on Nov. 13, 2001.

FIELD OF THE INVENTION

[0002] The present invention is directed to a method for determiningretail unit specific price sensitivities, and in particular to a methodthat directly links a weighted price index to profits and traffic andfurther eliminates seasonality effects by comparing year over yearchanges.

BACKGROUND ART

[0003] In the prior art, it is common to implement pricing or promotionstrategies for a chain of retail outlets. However, a problem oftenarises because one or more local unit managers complain that the overallpricing or promotion strategy does not apply to their stores; the “yes,but my store is different” syndrome. Often times, the local manager'sobservations are accurate due to the access to local information andexperience that is typically unavailable to corporate headquarters.

[0004] Consequently, there is a need to develop better techniques foridentifying the price sensitivities of a store or business unit. Thepresent invention solves this need by providing a method to permit theidentification of the price sensitivities of one or more stores. Withthis information, a business owner can determine whether a particularstore can raise prices or is too price sensitive and should concentrateon promotions rather than raising prices.

SUMMARY OF THE INVENTION

[0005] It is a first object of the present invention to provide a methodof identifying store price sensitivities for marketing purposes.

[0006] Another object of the invention is a method of identifying storeprice sensitivities that eliminates seasonal effects.

[0007] Still another object of the invention is a method that enables astore owner to better maximize profits through price promotions ratherthan higher prices or vice versa.

[0008] Other objects and advantages of the present invention will becomeapparent as a description thereof proceeds.

[0009] The store sensitivity analysis produces summary numbers forindividual units in a chain, allowing classification of units accordingto how price sensitive both profits and traffic are (sales are usedinstead of profits when sales are available and profits are not). Tworegressions can be used together or individually to categorize storesinto groups reflecting various pricing status and traffic sensitivitysimilarities. One is the gross profit regression and the other is thetraffic regression. These two regressions by themselves return valuableinformation on the pricing status and sensitivities of the stores in thesystem. Moreover, as the combination of these two regression results isused to categorize stores into groups that are homogenous, similarrevenue management and profit maximizing strategies may be employed oneach store in the category.

[0010] Stores that are determined to be price sensitive by the inventionin both profits and traffic should exercise care in raising prices, butopportunities to exploit price promotions may still exist. Stores thatare not price sensitive can be more aggressive in pricing across theboard.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Reference is now made to the drawings of the invention wherein:

[0012]FIG. 1a is a graph comparing gross profit function compared toprice;

[0013]FIG. 1b is a graph comparing quantity of items sold versus price;

[0014]FIG. 2 is a pie chart showing gross profit sensitivity to price;and

[0015]FIG. 3 is a pie chart showing traffic sensitivity to price.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The invention offers significant advantages in the field ofpricing and promotion strategies by being able to identify the pricesensitivity of a retail unit amongst a number of retail units.Identifying this price sensitivity provides invaluable information inpermitting an owner to better identify which marketing tools are bettersuited for that store.

[0017] The inventive method involves a number of steps, the stepsprincipally analyzing the affects of gross profit and traffic orcustomer count on pricing.

[0018] A first step involves developing a database of information overtime for each store in terms of various variables relating to pricesensitivity, e.g., prices, profits, sales, items being sold, quantity ofitems, time periods, for determining a fixed weight price index. Theindex is used to assist in analyzing gross profits and traffic forstores and sets of stores. In analyzing gross profit, a regressionanalysis is made wherein the analysis delivers a category measure ofpricing status: “Low” indicates that price increases are likely to leadto improvements in gross profit, the store can be more aggressive inpricing. “High” indicates that the store's prices are high and careneeds to be taken in considering future increases, i.e., the store isprice sensitive. It is important to understand that a pricingperformance categorization of “High” for the store does not limit thepricing strategy of that store from improving profits. On the contrary,it indicates that increased profits can still be realized, potentiallyby decreasing prices or increasing promotional activity usingappropriate items. “Right” indicates that the price level, as measuredby the sensitivity statistic, is about right; judicious price increasescan be made, but perhaps there are opportunities in manipulating menumix by pricing policy. “Insufficient evidence” indicates that theevidence for the store is mixed.

[0019] The second regression analysis focuses on traffic or customercount. This analysis delivers a category measure of traffic sensitivityin relation to the price index (how does quantity vary with the priceindex.) “Not Sensitive” indicates that the price increases have noadverse affects upon traffic. A flat or even estimated positive slope ofthe line basically indicates no relationship between traffic andincreases in price. “Sensitive” or “Highly Sensitive” reflects a trafficsensitivity to price increases that begins to evidence a downturn intraffic when increased prices are implemented, i.e., a negative slopeshowing that when the price index increases, traffic decreases.

[0020] While it is preferred to perform both regression analyses for acomplete picture of store sensitivity, either analysis could be donealone.

[0021] The following better demonstrates the impact that the inventiveanalysis framework could have on store profitability. Stores in the“Sensitive” traffic category and in the “High” price performancecategory are likely to see revenue gains from price decreases and shouldbe extremely cautious about increases. Those with “Insensitive” trafficand “Low” price performance are in line for price increases. Tounderstand how these measures affect store performance, consider thelikely shape of the gross profit function graphed as a function of priceas shown in FIGS. 1a and 1 b.

[0022] Referring to FIG. 1a, at prices below unit cost, of course grossprofit is negative (this pricing strategy is easy to rule out), and atprice equal to unit cost, labeled below (1) in FIG. 1a, gross profit iszero. As price increases from (1), gross profit can be expected toincrease, as long as consumers want the product at all. Now considerwhat happens as the price becomes high. Contribution margin increases,but the quantity sold can reasonably be expected to decline. At someprice, the quantity will be zero, and hence so will gross profit. Thisis below point (3) in FIG. 1a. As the two effects of prices operate, agross profit function of the general shape given in FIG. 1a can beproduced. Maximum gross profit (2) occurs at price p*. Of course, theseller would like to choose the price p*, at which gross profit ismaximized.

[0023] An important aspect of the inventive store sensitivity system isthe ability to examine data on prices and gross profits and determinewhether stores are operating at or near p*, at prices below p*, or atprices above p*. Identifying the store's relationship to p* gives thestore owner insight as to what should be done to improve profits.

[0024] In contrast to FIG. 1a, which focuses on the relationship betweengross profit and price, another aspect of the invention relates to therelationship between traffic or quantity sold and price. Thisrelationship can be used to illustrate the development of the grossprofit function from assumptions on consumer demand. Referring to FIG.1b, suppose that q represents the quantity sold of an item. Furthersuppose that the quantity sold q of the item in question is a functionof price. Suppose further for illustration that it is a linear functionq=a−b*p, as graphed in FIG. 1b. Assume that the per-unit cost is c(refer to FIG. 1a). Consider the price p* in FIG. 1b. At that price, thequantity sold is q*, from the demand function. Revenue realized is p*times q* namely the area of the indicated rectangles 1 and 2. Cost (foodcost) is given by q* times c, also indicated as the area of rectangle 1on the graph. The difference between these areas is exactly gross profitor rectangle 2. Thus FIG. 1a, the gross profit function, can bedeveloped from FIG. 1b by considering different prices, reading thecorresponding quantities from the demand function, calculating revenueand cost and taking the difference for gross profit, and graphing grossprofit against prices. However, this method is quite tedious andrequires a number of steps to arrive at gross profit based on individualproducts. In the store sensitivity analysis of the invention, grossprofit is studied directly; not via the demands for individual products.This is a tremendous simplification and advantage when dealing withmulti-product situations.

[0025] The stylized case of a firm selling one product in varyingquantities provides a useful framework for focusing ideas, butimplementation in the case of restaurants with full menus or retailstores with full product lines are different situations entirely. Thestore sensitivity approach emphasizes restaurant-level characteristics,not item-level characteristics.

[0026] In order to develop a single summary measure of pricing status,it is preferred to develop a single index summarizing the prices in aparticular store. This index of prices can be calculated for individualstores over many periods, and the relation between the price index and ameasure of gross profit can be examined on the basis of co-variationbetween the two variables.

[0027] When dealing with indices, one question to consider is the use ofweighted averages of the prices of the different menu items as a summarymeasure of the prices in a given store in a given period. If a weightedindex is selected, the question then becomes what weights should beused. One possibility is to weight by menu mix. In this case, the indexis simply the check average defined as total revenue divided by totalitems sold ($10.00 in revenue/5 items=2.0). This calculation involvesthe use of the price of the items weighted by the number sold. Theproblem with this approach is that because menu mix changes from periodto period as consumer purchasing behavior varies, changes in the checkaverage will occur even when prices have not moved. Put another way,while prices may stay the same, the number of items may change, thuschanging the price index.

[0028] The present invention avoids this pitfall through the use anindex, which is an indicator of movements of prices within the store'scontrol. The check average mixes up changes in prices and changes inquantities sold from period to period and is therefore not desirable. Afixed-weighted index is preferred since it does not suffer from theproblems of a check average and is more appropriate for determiningprice sensitivities. Fixed weight indices are well known in thestatistic art, and a detailed explanation is not deemed necessary forunderstanding of the invention.

[0029] It is preferred to weight the different prices by a measure ofthe relative importance of each price in revenue production. Theinventive store sensitivity analysis approach uses a fixed-weight systemin which the weights are the average menu mix per store over the periodconsidered. This method produces an index which moves only when pricesmove, but which still does weight prices according to their revenuecontribution. This technique does not use the check average approach,which can move even if prices do not.

[0030] Referring back to the regression analysis of gross profit orquantity sold, a number of store/period specific variables for use inthe analysis include: (1) lnpp, the logarithm of profits (these can beactual profits, or a measure adjusted for changes in costs, or ifprofits are unavailable, sales; (2) lntraffic, the logarithm of ameasure of traffic (either customer counts or number of items sold); and(3) lnpind, the logarithm of the price index constructed as describedabove. Periods can vary such as by day, week or month.

[0031] In the ideal, full data case, data are available for more thanone year. Having data for more than a year allows new variables to beformed, i.e., dlnpp, dlntraffic, and dlnpind, the year over year changesin each of these variables. For example, dlnpp can represent thedifference between profits in week 27 in the current year and week 27 ofthe previous year. The regression coefficient of dlnpind in theregression of dlnpp on dlnpind is the price sensitivity index(corresponding approximately to the slope of the function shown in FIG.1a). The coefficient in the regression of dlntraffic on dlnpind is thepromotion or traffic sensitivity index. This year over year comparisonis a significant advantage when determining true price sensitivities. Bylooking at the difference in gross profit and traffic in the same seasonbut between two different years, the potential confounding effects ofseasonality are eliminated in the estimate. The regressions arepreferably performed separately for each store if the data permit;however, importantly, this specification in differences allows combininginformation across similar stores to obtain an overall “market”sensitivity for any commercially interesting group of stores.

[0032] The regression can also be done without year over year data,e.g., lnpp on lnpind, by store for a selected period of time.

[0033] Once the regression coefficients are generated, a summary reportfor the chain as a whole can be developed which is of significantimportance in determining the price and traffic sensitivity for allstores. An example in terms of a restaurant is shown below. While notshown, a similar report could be which would show a listing of theparticular results by store, e.g., what stores are highly sensitive, notsensitive, etc. in traffic and which stores are high, low, or right interms of gross profit.

[0034] The FIGS. 2 and 3 summarize the chain's gross profit and trafficsensitivity for US restaurants only.

[0035]FIG. 2 represents gross profits and illustrates that 47% of all USstores in this example have a “Low” gross profit sensitivity. Thisindicates that these stores are performing below the optimal grossprofit point and there are significant profit opportunities remainingwithin these stores. Eighteen percent of the stores are operating at theright gross profit point, and 25% are operating beyond the optimal grossprofit point. There was insufficient evidence to determine thesensitivity ratings for 10% of the stores.

[0036] Referring to FIG. 3, stores characterized by “Not Sensitive” toprice do not drive traffic through price promotions, while stores thatare “Highly Sensitive” to price can improve traffic with pricepromotions on items. Stores characterized by “Low” gross profitsensitivity and “Low” traffic sensitivity have an opportunity forincreased margins by increasing prices on the proper items. The secondgroup of stores, evidencing “High” gross profit sensitivity and “High”traffic sensitivity must exercise caution when implementing pricechanges and may do better with price promotions.

[0037] As noted above, a final part of the report is the list of storesand their categorizations. Stores with “insufficient evidence” simply donot have enough data variation to identify sensitivities (i.e. theregression t-statistics are <1.5 in absolute value; this number can bevaried according to the level of confidence required). Sensitive storeshave significantly negative coefficients, and insensitive stores havezero or positive coefficients.

[0038] While the invention is described in terms of gross profits, thismeasure is not always available. In these instances, sales can besubstituted for profits.

[0039] While the example uses variables based on the difference in yearto year, other time periods could be used such as week to adjacent week,month to adjacent month, day to adjacent day, year to adjacent year,etc.

[0040] As such, an invention has been disclosed in terms of preferredembodiments thereof which fulfills each and every one of the objects ofthe present invention as set forth above and provides new and improvedmethod for determining price sensitivities for retail units.

[0041] Of course, various changes, modifications and alterations fromthe teachings of the present invention may be contemplated by thoseskilled in the art without departing from the intended spirit and scopethereof. It is intended that the present invention only be limited bythe terms of the appended claims.

What is claimed is:
 1. A method of determining a price sensitivity forone or more retail units comprising: a) creating a fixed weight priceindex based on pricing information from each retail unit, wherein theindex varies only when prices vary and the weights are based on anaverage menu mix per retail unit over a select period of time; b)regressing at least one of profits or sales or quantity sold for theretail unit on the fixed weight price index over a select period oftime, and producing a regression coefficient for the fixed weight priceindex, wherein time differences in profits/sales and time differences inthe price index are used as the independent variables in the regressionanalysis and the variable regressed is the time differences in quantitysold and gross profit; and c) assigning a price sensitivity indicatorbased on the magnitude of the regression coefficient, wherein themagnitude of the indicator reflects the level of price sensitivity ofthe retail unit as it relates to the regressed variable.
 2. The methodof claim 1, wherein each of profits or sales and quantity sold areregressed, and the indicator for profits/sales shows how the storecompares to an optimum pricing index, and the indicator for quantitysold shows how sensitive the store is to price changes.
 3. The method ofclaim 1, wherein price sensitivity indicators for profits/sales includehigh, low, and right.
 4. The method of claim 1, wherein pricesensitivity indicators for quantity sold include not sensitive,sensitive, and highly sensitive.
 5. The method of claim 1, wherein thetime difference is one of a year to year time difference, a week to anadjacent week time difference, a day to an adjacent day, or a month toan adjacent month.
 6. The method of claim 1, where the time differenceis based on a year to year time difference.
 7. The method of claim 1,wherein a log of the profits or sales or quantity sold for the retailunit are regressed on a log of the fixed weight price index.
 8. A methodof determining a price sensitivity of one or more retail unitscomprising: identifying a weighted price index for each retail unit fora period of time; regressing gross profits of the retail unit on theweighted price index to determine where the weighted price index fallswith respect to the gross profit function in order to ascertain amagnitude of price sensitivity against gross profit for the retail unit.9. A method of determining a price sensitivity of one or more retailunits comprising: identifying a weighted price index for each retailunit for a period of time; regressing quantity of items sold for theretail unit on the weighted price index to determine where the weightedprice index falls with respect to the quantity sold in order toascertain a magnitude of price sensitivity against quantity of itemssold for the retail unit.
 10. A method of claim 9, further comprisingregressing quantity of items sold for the retail unit on the weightedprice index to determine where the weighted price index falls withrespect to the quantity sold in order to ascertain a magnitude of pricesensitivity against quantity of items sold for the retail unit.
 11. Themethod of claim 9, further comprising assigning a gross profit indicatorto reflect where the weighted price index falls with respect to thegross profit.
 12. The method of claim 9, further comprising assigning asensitivity indicator to reflect where the weighted price index fallswith respect to the quantity of items sold.
 13. The method of claim 9,wherein the period of time is a year to year time period, and theregression is based on the year to year differences in the weightedprice index.
 14. The method of claim 10, further comprising assigning agross profit indicator to reflect where the weighted price index fallswith respect to the gross profit.
 15. The method of claim 10, furthercomprising assigning a sensitivity indicator to reflect where theweighted price index falls with respect to the quantity of items sold16. The method of claim 10, wherein the period of time is a year to yeartime period, and the regression is based on the year to year differencesin the weighted price index
 17. The method of claim 11, wherein theperiod of time is a year to year time period, and the regression isbased on the year to year differences in the weighted price index 18.The method of claim 12, wherein the period of time is a year to yeartime period, and the regression is based on the year to year differencesin the weighted price index